Natural Gas Market Hubs and Interstate Pipeline Corridors¶

Data Import¶

In [26]:
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import folium
import contextily as cx
import rtree
from zlib import crc32
import hashlib
from shapely.geometry import Point, LineString, Polygon

Natural Gas Pipeline Data¶

In [27]:
## Importing our DataFrames

#gisfilepath = "/Users/jnapolitano/Projects/data/energy/Natural_Gas_Pipelines.geojson"
gisfilepath = '/Users/jnapolitano/Projects/data/energy/Natural_Gas_Liquid_Pipelines.zip'

ng_pipeline_df = gpd.read_file(gisfilepath)

ng_pipeline_df = ng_pipeline_df.to_crs(epsg=3857)

#colums = ng_pipeline_df.columns
#uniqe = ng_market_df.TYPE.unique()
ng_pipeline_df = ng_pipeline_df[ng_pipeline_df.TYPEPIPE == 'Interstate']
ng_pipeline_df.dropna(inplace=True)
ng_pipeline_df.head()
Out[27]:
TYPEPIPE Operator Shape_Leng geometry
737 Interstate Enbridge Pipelines (AlaTenn) 0.171766 LINESTRING (-9656362.029 4119699.311, -9653795...
738 Interstate Enbridge Pipelines (AlaTenn) 0.174189 LINESTRING (-9675726.277 4118469.233, -9673070...
739 Interstate Enbridge Pipelines (AlaTenn) 0.175478 LINESTRING (-9675726.277 4118469.233, -9683221...
740 Interstate Enbridge Pipelines (AlaTenn) 0.057599 LINESTRING (-9682131.156 4118105.433, -9675726...
741 Interstate Enbridge Pipelines (AlaTenn) 0.099992 LINESTRING (-9692746.582 4114035.142, -9690740... 
{eval-rst}

.. index::
   single: Natural Gas Terrain Interactive Map

Natural Gas By Operator Map Data¶

In [28]:
ng_pipeline_map =ng_pipeline_df.explore(
    #column="Operator", # make choropleth based on "PORT_NAME" column
     popup=False, # show all values in popup (on click)
     tiles='Stamen Terrain',
     #tiles="CartoDB positron", # use "CartoDB positron" tiles
     #cmap='Reds', # use "Set1" matplotlib colormap
     #m=ng_pipeline_map,
     #style_kwds=dict(color="black"),
     #marker_kwds= dict(radius=2),
     #tooltip=['','State','Hub_name','Operator','Maxthru','Avgdaily','Numcust','Platform'],
     #legend =False, # use black outline)
     #categorical=True,
     color='grey'
    )
#ng_pipeline_map

Natural Gas Market Hub Data¶

In [29]:
## Importing our DataFrames

gisfilepath = "/Users/jnapolitano/Projects/data/energy/Natural_Gas_Market_Hubs.geojson"

ng_market_df = gpd.read_file(gisfilepath)

ng_market_df = ng_market_df.to_crs(epsg=3857)

ng_market_df.describe()
Out[29]:
FID OBJECTID Yr_activat Maxthru Avgdaily Numcust Yearofdata Latitude Longitude
count 27.000000 27.000000 27.000000 27.000000 27.000000 27.000000 27.000000 27.000000 27.000000
mean 14.000000 14.000000 1995.333333 1409.111111 951.481481 74.777778 2007.888889 36.538033 -100.874230
std 7.937254 7.937254 4.047791 959.288492 769.353893 133.527391 1.012739 6.368855 13.339038
min 1.000000 1.000000 1988.000000 0.000000 0.000000 0.000000 2003.000000 27.548800 -122.220000
25% 7.500000 7.500000 1994.000000 615.000000 320.000000 10.000000 2008.000000 30.213150 -109.284500
50% 14.000000 14.000000 1994.000000 1200.000000 600.000000 40.000000 2008.000000 36.686400 -97.600300
75% 20.500000 20.500000 1996.000000 2112.500000 1525.000000 61.000000 2008.000000 41.478950 -92.024700
max 27.000000 27.000000 2008.000000 3100.000000 2500.000000 660.000000 2009.000000 49.000500 -74.178500

Pipelines and Market Hubs Map¶

{eval-rst}

.. index::
   single: Pipelines by Market Hub Interactive Map
In [30]:
ng_market_map =ng_market_df.explore(
    column="Hub_name", # make choropleth based on "PORT_NAME" column
     popup=False, # show all values in popup (on click)
     tiles="Stamen Terrain", # use "CartoDB positron" tiles
     cmap='Reds', # use "Set1" matplotlib colormap
     m=ng_pipeline_map,
     #style_kwds=dict(color="black"),
     marker_kwds= dict(radius=15),
     tooltip=['Region','State','Hub_name','Operator','Maxthru','Avgdaily','Numcust','Platform'],
     legend =True, # use black outline)
     categorical=True,
    )


ng_market_map
Out[30]:
Make this Notebook Trusted to load map: File -> Trust Notebook